SoroushVahidi/scidocs huggingface dataset:
Printed $\lambda/8$ -PIFA for Penta-Band WWAN Operation in the Mobile Phone
(2009)

A small-size printed planar inverted-F antenna (PIFA) operated at its one-eighth wavelength (lambda/8) mode as the fundamental resonant mode for achieving WWAN (wireless wide area network) operation in the mobile phone is presented. The proposed PIFA has a simple structure of comprising two radiating strips of length about lambda/8 at 900 MHz and is fed using a coupling feed. Compared to the traditional PIFA using a direct feed, the coupling feed greatly decreases the very large input impedance seen at the lambda/8 mode for the traditional PIFA and results in successful excitation of the lambda/8 mode for the proposed PIFA. Two lambda/8 modes are generated by the two radiating strips and occur at close frequencies at about 900 MHz to form a wide lower band to cover GSM850/900 operation. The two radiating strips also generate two higher-order modes or lambda/4 modes at about 1900 MHz to form a wide upper band for GSM1800/1900/UMTS operation. Penta-band WWAN operation is hence achieved, yet the proposed PIFA only occupies a small printed area of 15times31 mm2 or 465 mm2 on the system circuit board of the mobile phone, which is about the smallest for the internal uniplanar printed antenna capable of penta-band operation that have been reported. Details of the proposed PIFA are presented. The specific absorption rate (SAR) and hearing aid compatibility (HAC) results for the proposed PIFA are also analyzed.
Learning to Doodle with Stroke Demonstrations and Deep Q-Networks
(2018)

Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to draw real and more complex doodles without ground truth actions; thus, it is trained with Qlearning. Our experiments confirm that (1) doodling can be learned without direct stepby-step action supervision and (2) pretraining with stroke demonstration via supervised learning is important to improve performance. We further show that Doodle-SDQ is effective at producing plausible drawings in different media types, including sketch and watercolor. A short video can be found at https://www.youtube.com/watch? v=-5FVUQFQTaE.
Automated Music Emotion Recognition: A Systematic Evaluation
(2010)

Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval (MIR) with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the ArousalValence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect induction rather than affect attribution. We consider a variety of algorithms at each stage of the training process, from preprocessing to feature selection and model selection, and we report the results of extensive testing. We found that: (1) none of the variations we considered leads to a substantial improvement in performance, which we present as evidence of a limit on what is achievable under this architecture, and (2) the size of the small data sets that are commonly used in the MER literature limits the possibility of improving the set of features used in MER due to the phenomenon of Subset Selection Bias. We conclude with some proposals for advancing the state of the art.
Efficient Hardware Architectures for Deep Convolutional Neural Network
(2018)

Convolutional neural network (CNN) is the state-of-the-art deep learning approach employed in various applications. Real-time CNN implementations in resource limited embedded systems are becoming highly desired recently. To ensure the programmable flexibility and shorten the development period, field programmable gate array is appropriate to implement the CNN models. However, the limited bandwidth and on-chip memory storage are the bottlenecks of the CNN acceleration. In this paper, we propose efficient hardware architectures to accelerate deep CNN models. The theoretical derivation of parallel fast finite impulse response algorithm (FFA) is introduced. Based on FFAs, the corresponding fast convolution units (FCUs) are developed for the computation of convolutions in the CNN models. Novel data storage and reuse schemes are proposed, where all intermediate pixels are stored on-chip and the bandwidth requirement is reduced. We choose one of the largest and most accurate networks, VGG16, and implement it on Xilinx Zynq ZC706 and Virtex VC707 boards, respectively. We achieve the top-5 accuracy of 86.25% using an equal distance non-uniform quantization method. It is estimated that the average performances are 316.23 GOP/s under 172-MHz working frequency on Xilinx ZC706 and 1250.21 GOP/s under 170-MHz working frequency on VC707, respectively. In brief, the proposed design outperforms the existing works significantly, in particular, surpassing related designs by more than two times in terms of resource efficiency.
Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results
(2004)

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum. Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.
The Iowa Model of Evidence-Based Practice to Promote Quality Care.
(2001)

The UIHC Department of Nursing is nationally known for its work on use of research to improve patient care. This reputation is attributable to staff members who continue to question "how can we improve practice?" or "what does the latest evidence tell us about this patient problem?" and to administrators who support, value, and reward EBP. The revisions made in the original Iowa Model are based on suggestions from staff at UIHC and other practitioners across the country who have implemented the model. We value their feedback and have set forth this revised model for evaluation and adoption by others.
Coupled semi-supervised learning for information extraction
(2010)

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.
Context adaptive deep neural networks for fast acoustic model adaptation
(2015)

Deep neural networks (DNNs) are widely used for acoustic modeling in automatic speech recognition (ASR), since they greatly outperform legacy Gaussian mixture model-based systems. However, the levels of performance achieved by current DNN-based systems remain far too low in many tasks, e.g. when the training and testing acoustic contexts differ due to ambient noise, reverberation or speaker variability. Consequently, research on DNN adaptation has recently attracted much interest. In this paper, we present a novel approach for the fast adaptation of a DNN-based acoustic model to the acoustic context. We introduce a context adaptive DNN with one or several layers depending on external factors that represent the acoustic conditions. This is realized by introducing a factorized layer that uses a different set of parameters to process each class of factors. The output of the factorized layer is then obtained by weighted averaging over the contribution of the different factor classes, given posteriors over the factor classes. This paper introduces the concept of context adaptive DNN and describes preliminary experiments with the TIMIT phoneme recognition task showing consistent improvement with the proposed approach.
ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging
(2013)

As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided.
CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks
(1986)

MC& CYC project is the building, over the coming decade, of a large knowledge base (or KB) of real world facts and heuristics and-as a part of the KB itself-methods for efficiently reasoning over the KB. As the title of this article suggests, our hypothesis is that the two major limitations to building large intelligent programs might be overcome by using such a system. We briefly illustrate how common sense reasoning and analogy can widen the knowledge acquisition bottleneck The next section (“How CYC Works”) illustrates how those same two abilities can solve problems of the type that stymie current expert systems. We then report how the project is being conducted currently: its strategic philosophy, its tactical methodology, and a case study of how we are currently putting that into practice. We conclude with a discussion of the project’s feasibility and timetable.
Privacy-Preserving Attribute-Based Access Control Model for XML-Based Electronic Health Record System
(2018)

Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient’s document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.
An empirical approach to the experience of architectural space in virtual reality — exploring relations between features and affective appraisals of rectangular indoor spaces
(2005)

In the presented exploratory study, quantitative relations between the experience of architectural spaces and physical properties were investigated using virtual reality (VR) simulations. Geometric properties from a component-based description of rectangular rooms were tested on their suitability as predictor variables for experiential qualities. In a virtual-reality-based perceptual experiment, qualities of 16 vacant rectangular interiors were rated in eight principal categories by 16 participants using the semantic differential scaling technique. The simulated scenes were automatically generated by means of a custom made utility that also provided for the component-based room descriptions. The data analysis revealed strong correlations between several scene features and the averaged rated experience. For example, a preference for ratios near to the golden section could be observed for spatial proportions, which are not directly perceivable. Altogether, a set of five widely independent factors (openness, two room proportions, room area, and balustrade height) turned out to be most effective for describing the observed variance in the attributed experiential qualities. The combination of realistic virtual reality simulations and psychophysical data raising methods proved to be an effective means for basic architectural research. Several quantitative relations between physical properties and the emotional experience of architectural space could be demonstrated. D 2004 Elsevier B.V. All rights reserved.
Mutual Coupling Reduction Using F-Shaped Stubs in UWB-MIMO Antenna
(2018)

A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling is presented in this paper. The proposed antenna consists of two radiating elements with shared ground plane having an area of <inline-formula> <tex-math notation="LaTeX">$50\times 30$ </tex-math></inline-formula> mm<sup>2</sup>. F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. The designed MIMO antenna has very low mutual coupling of (S<sub>21</sub> < −20 dB), low envelop correlation coefficient (ECC <0.04), high diversity gain (DG >7.4 dB), high multiplexing efficiency (<inline-formula> <tex-math notation="LaTeX">${\eta }_{\mathrm {Mux}}> -3.5$ </tex-math></inline-formula>), and high peak gain over the entire UWB frequencies. The antenna performance is studied in terms of S-Parameters, radiation properties, peak gain, diversity gain, envelop correlation coefficient, and multiplexing efficiency. A good agreement between the simulated and measured results is observed.
Building a Literal Bridge Between Robotics and Neuroscience using Functional Near Infrared Spectroscopy ( NIRS )
(2014)

Functional near infrared spectroscopy (NIRS) is a promising new tool for research in human-robot interaction (HRI). The use of NIRS in HRI has already been demonstrated both as a means for investigating brain activity during human-robot interactions, as well as in the development of brain-robot interfaces that passively monitor a person’s cognitive state to adapt robots’ behaviors accordingly. In this paper, we survey the utility of NIRS and its range of applications in HRI. We discuss both some exemplary applications as well as several pressing challenges to the deployment of NIRS in more realistic settings.
Automatic Code Completion
(2017)

Code completion software is an important tool for many developers, but it traditionally fails to model any long term program dependencies such as scoped variables, instead settling for suggestions based on static libraries. In this paper we present a deep learning approach to code completion for non-terminals (program structural components) and terminals (program text) that takes advantage of running dependencies to improve predictions. We develop an LSTM model and augment it with several approaches to Attention in order to better capture the relative value of the input, hidden state, and context. After evaluating on a large dataset of JavaScript programs, we demonstrate that our Gated LSTM model significantly improves over a Vanilla LSTM baseline, achieving an accuracy of 77% on the non-terminal prediction problem and 46% accuracy on the terminal prediction problem.
Could on-line voting boost desire to vote? - Technology acceptance perceptions of young Hungarian citizens
(2016)

Article history: Received 24 May 2015 Received in revised form 2 October 2016 Accepted 18 November 2016 In our paper we develop and test the argument that intent to i-vote (to use on-line voting systems) drives intent to vote, while intent to i-vote is influenced by four key attitudes: performance expectation, perception on ease of use, trust in the internet and trust in the government.We show that these findings contradict thosewhich exclusively identified economical, legal, and cultural drivers to enhance democratic participation in the Central and Eastern European region. Rooted cardinally in the Technology Acceptance Model (TAM) six hypotheses were set, and then tested with partial least square (PLS) structural equation modelling. In the context of young, educated and internet-ready Hungarian voters the testing of the hypotheses has shown high level of on-line voting intent and that perception of on-line voting would enhance voting desire amongst young Hungarian internet users. Also, our findings show that performance expectation, perception on ease of use and trust in the internet are positively associated with i-voting intent. © 2016 Elsevier Inc. All rights reserved.
Feature selection with harmony search and its applications
(2014)

Feature selection is a term given to the problem of selecting important domain attributes which are most predictive of a given outcome. Unlike other dimensionality reduction methods, feature selection approaches seek to preserve the semantics of the original data following reduction. Many strategies have been exploited for this task in an effort to identify more compact and better quality feature subsets. A number of group-based feature subset evaluation measures have been developed, which have the ability to judge the quality of a given feature subset as a whole, rather than assessing the qualities of individual features. Techniques of stochastic nature have also emerged, which are inspired by nature phenomena or social behaviour, allowing good solutions to be discovered without resorting to exhaustive search. In this thesis, a novel feature subset search algorithm termed “feature selection with harmony search” is presented. The proposed approach utilises a recently developed meta-heuristic: harmony search, that is inspired by the improvisation process of musical players. The proposed approach is general, and can be employed in conjunction with many feature subset evaluation measures. The simplicity of harmony search is exploited to reduce the overall complexity of the search process. The stochastic nature of the resultant technique also allows the search process to escape from local optima, while identifying multiple, distinctive candidate solutions. Additional parameter control schemes are introduced to reduce the effort and impact of static parameter configuration of HS, which are further combined with iterative refinement, in order to enforce the discovery of more compact feature subsets. The flexibility of the proposed approach, and its powerful performance in selecting multiple, good quality feature subsets lead to a number of further theoretical developments. These include the generation and reduction of feature subset-based classifier ensembles; feature selection and adaptive classifier ensemble for dynamic data; hybrid rule induction on the basis of fuzzy-rough set theory; and antecedent selection for fuzzy rule interpolation. The resultant techniques are experimentally evaluated using data sets drawn from real-world problem domains, and systematically compared with leading methodologies in their respective areas, demonstrating the efficacy and competitive performance of the present work.
Text summarization using enhanced MMR technique
(2014)

Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.
Modeling temporal effects of human mobile behavior on location-based social networks
(2013)

The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.